Project Summary Abstract This proposal is for a renewal of NIH funding for Washington University?s Genome Analysis Training Program (GATP). The overall goal of the GATP is to train a diverse group of multidisciplinary, quantitatively sophisticated leaders in genomic technology, science, and medicine. In this renewal, we are focusing exclusively on training predoctoral students, because Washington University has an outstanding pool of highly talented PhD and MD/PhD students from which we can recruit the very best for the GATP. We are requesting funds to support 8 predoctoral trainees. If we are granted these slots the university will provide matching funds to support an additional 4 trainees. The GATP is designed to produce trainees who are sophisticated in their knowledge of both the experimental and the mathematical / computational aspects of genome science. This is achieved through a rigorous set of required classes and through research-based training. We have an outstanding group of 37 training faculty who run world-class research labs and provide careful mentoring to our trainees. Our PhD programs in Computational Biology and Genetics/Genomics were jointly ranked #1 in the nation in the 2018 US News & World Report analysis, tied with MIT and Stanford. We have made significant changes to our training program based on feedback from our trainees. Our trainees consistently express an interest in learning about career paths that utilize their skills. In response we have designed a set of mini-internship programs that take advantage of our connections to the pharmaceutical and biotechnology industries, and to clinical genomics services. The opportunities are aimed at cultivating the leadership capability of our trainees and fostering a broad understanding of the different work environments and career paths in which genomics plays an important role. Many of our trainees are also interested in how machine learning is applied to problems in the genome sciences. As such we made changes to the GATP that ensure that all trainees are exposed to the foundational concepts underlying machine learning and artificial intelligence. We have also made changes to our evaluation protocols to make sure that we are meeting our goals in a way that is responsive to our trainees.